Overview

Dataset statistics

Number of variables16
Number of observations973797
Missing cells169356
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory118.9 MiB
Average record size in memory128.0 B

Variable types

Numeric12
Categorical4

Alerts

time has a high cardinality: 40495 distinct values High cardinality
gameId is highly correlated with teamHigh correlation
frameId is highly correlated with s and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
team is highly correlated with gameIdHigh correlation
nflId has 42339 (4.3%) missing values Missing
jerseyNumber has 42339 (4.3%) missing values Missing
o has 42339 (4.3%) missing values Missing
dir has 42339 (4.3%) missing values Missing
s has 62663 (6.4%) zeros Zeros
a has 58410 (6.0%) zeros Zeros
dis has 61876 (6.4%) zeros Zeros

Reproduction

Analysis started2022-11-02 15:00:23.316286
Analysis finished2022-11-02 15:01:51.668211
Duration1 minute and 28.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021101693
Minimum2021101400
Maximum2021101800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:51.713923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2021101400
5-th percentile2021101400
Q12021101702
median2021101706
Q32021101709
95-th percentile2021101800
Maximum2021101800
Range400
Interquartile range (IQR)7

Descriptive statistics

Standard deviation80.73039883
Coefficient of variation (CV)3.994375895 × 10-8
Kurtosis8.362886128
Mean2021101693
Median Absolute Deviation (MAD)3
Skewness-2.887787025
Sum1.968142765 × 1015
Variance6517.397296
MonotonicityIncreasing
2022-11-02T12:01:51.811045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
202110170782570
 
8.5%
202110170281259
 
8.3%
202110170080661
 
8.3%
202110170980546
 
8.3%
202110170475969
 
7.8%
202110180073278
 
7.5%
202110170671415
 
7.3%
202110171069598
 
7.1%
202110170863779
 
6.5%
202110170163181
 
6.5%
Other values (4)231541
23.8%
ValueCountFrequency (%)
202110140062537
6.4%
202110170080661
8.3%
202110170163181
6.5%
202110170281259
8.3%
202110170360030
6.2%
202110170475969
7.8%
202110170552739
5.4%
202110170671415
7.3%
202110170782570
8.5%
202110170863779
6.5%
ValueCountFrequency (%)
202110180073278
7.5%
202110171156235
5.8%
202110171069598
7.1%
202110170980546
8.3%
202110170863779
6.5%
202110170782570
8.5%
202110170671415
7.3%
202110170552739
5.4%
202110170475969
7.8%
202110170360030
6.2%

playId
Real number (ℝ≥0)

Distinct903
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2172.958596
Minimum55
Maximum5223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:51.934487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile257
Q11054
median2145
Q33245
95-th percentile4158
Maximum5223
Range5168
Interquartile range (IQR)2191

Descriptive statistics

Standard deviation1269.613603
Coefficient of variation (CV)0.5842787826
Kurtosis-1.108311386
Mean2172.958596
Median Absolute Deviation (MAD)1099
Skewness0.09770258967
Sum2116020562
Variance1611918.701
MonotonicityNot monotonic
2022-11-02T12:01:52.062605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9234186
 
0.4%
38372806
 
0.3%
10632806
 
0.3%
27392760
 
0.3%
17572714
 
0.3%
39062691
 
0.3%
17782645
 
0.3%
552599
 
0.3%
31302576
 
0.3%
14272553
 
0.3%
Other values (893)945461
97.1%
ValueCountFrequency (%)
552599
0.3%
56667
 
0.1%
62989
 
0.1%
63897
 
0.1%
732185
0.2%
76667
 
0.1%
83874
 
0.1%
94690
 
0.1%
95713
 
0.1%
961173
0.1%
ValueCountFrequency (%)
5223989
0.1%
51331196
0.1%
50871196
0.1%
5036782
0.1%
4883782
0.1%
4801989
0.1%
47751219
0.1%
4746598
0.1%
4692874
0.1%
4663713
0.1%

nflId
Real number (ℝ≥0)

MISSING

Distinct1025
Distinct (%)0.1%
Missing42339
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean45822.98825
Minimum25511
Maximum53957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:52.190456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25511
5-th percentile37724
Q142476
median46070
Q348034
95-th percentile53496
Maximum53957
Range28446
Interquartile range (IQR)5558

Descriptive statistics

Standard deviation4982.562942
Coefficient of variation (CV)0.1087350069
Kurtosis-0.01482357479
Mean45822.98825
Median Absolute Deviation (MAD)3239
Skewness-0.1889764522
Sum4.268218899 × 1010
Variance24825933.47
MonotonicityNot monotonic
2022-11-02T12:01:52.309872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
385922427
 
0.2%
478102427
 
0.2%
386422427
 
0.2%
534722427
 
0.2%
524912427
 
0.2%
478242427
 
0.2%
433842427
 
0.2%
412582427
 
0.2%
461092216
 
0.2%
529382181
 
0.2%
Other values (1015)907645
93.2%
(Missing)42339
 
4.3%
ValueCountFrequency (%)
255111162
0.1%
289631306
0.1%
295501541
0.2%
298511069
0.1%
30842540
 
0.1%
308691014
0.1%
331071139
0.1%
33130429
 
< 0.1%
331311194
0.1%
344521130
0.1%
ValueCountFrequency (%)
539571278
0.1%
539531497
0.2%
53946324
 
< 0.1%
53921666
0.1%
5391035
 
< 0.1%
53900594
 
0.1%
5387681
 
< 0.1%
53854511
 
0.1%
53687481
 
< 0.1%
5367831
 
< 0.1%

frameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct182
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.37246983
Minimum1
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:52.440666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile51
Maximum182
Range181
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.11098948
Coefficient of variation (CV)0.689314805
Kurtosis7.11016077
Mean23.37246983
Median Absolute Deviation (MAD)11
Skewness1.526384812
Sum22760041
Variance259.5639821
MonotonicityNot monotonic
2022-11-02T12:01:52.561051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123092
 
2.4%
223092
 
2.4%
2023092
 
2.4%
1923092
 
2.4%
1823092
 
2.4%
1723092
 
2.4%
1623092
 
2.4%
1523092
 
2.4%
1423092
 
2.4%
1323092
 
2.4%
Other values (172)742877
76.3%
ValueCountFrequency (%)
123092
2.4%
223092
2.4%
323092
2.4%
423092
2.4%
523092
2.4%
623092
2.4%
723092
2.4%
823092
2.4%
923092
2.4%
1023092
2.4%
ValueCountFrequency (%)
18223
< 0.1%
18123
< 0.1%
18023
< 0.1%
17923
< 0.1%
17823
< 0.1%
17723
< 0.1%
17623
< 0.1%
17523
< 0.1%
17423
< 0.1%
17323
< 0.1%

time
Categorical

HIGH CARDINALITY

Distinct40495
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
2021-10-17T17:25:37.800
 
69
2021-10-17T17:25:39.100
 
69
2021-10-17T19:44:31.900
 
69
2021-10-17T19:44:32.000
 
69
2021-10-17T19:44:32.200
 
69
Other values (40490)
973452 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters22397331
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-15T00:23:39.200
2nd row2021-10-15T00:23:39.300
3rd row2021-10-15T00:23:39.400
4th row2021-10-15T00:23:39.500
5th row2021-10-15T00:23:39.600

Common Values

ValueCountFrequency (%)
2021-10-17T17:25:37.80069
 
< 0.1%
2021-10-17T17:25:39.10069
 
< 0.1%
2021-10-17T19:44:31.90069
 
< 0.1%
2021-10-17T19:44:32.00069
 
< 0.1%
2021-10-17T19:44:32.20069
 
< 0.1%
2021-10-17T17:25:38.50069
 
< 0.1%
2021-10-17T17:25:38.60069
 
< 0.1%
2021-10-17T17:25:38.70069
 
< 0.1%
2021-10-17T17:25:38.80069
 
< 0.1%
2021-10-17T17:25:38.90069
 
< 0.1%
Other values (40485)973107
99.9%

Length

2022-11-02T12:01:52.856045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-10-17t17:25:37.80069
 
< 0.1%
2021-10-17t18:11:25.20069
 
< 0.1%
2021-10-17t18:11:25.00069
 
< 0.1%
2021-10-17t19:44:31.80069
 
< 0.1%
2021-10-17t19:44:32.10069
 
< 0.1%
2021-10-17t19:44:31.60069
 
< 0.1%
2021-10-17t17:25:38.10069
 
< 0.1%
2021-10-17t19:44:31.50069
 
< 0.1%
2021-10-17t17:25:37.70069
 
< 0.1%
2021-10-17t17:25:37.90069
 
< 0.1%
Other values (40485)973107
99.9%

Most occurring characters

ValueCountFrequency (%)
04833703
21.6%
14196856
18.7%
22957432
13.2%
-1947594
8.7%
:1947594
8.7%
71229051
 
5.5%
T973797
 
4.3%
.973797
 
4.3%
3690161
 
3.1%
5674383
 
3.0%
Other values (4)1972963
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16554549
73.9%
Other Punctuation2921391
 
13.0%
Dash Punctuation1947594
 
8.7%
Uppercase Letter973797
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04833703
29.2%
14196856
25.4%
22957432
17.9%
71229051
 
7.4%
3690161
 
4.2%
5674383
 
4.1%
4654166
 
4.0%
9546365
 
3.3%
8465520
 
2.8%
6306912
 
1.9%
Other Punctuation
ValueCountFrequency (%)
:1947594
66.7%
.973797
33.3%
Dash Punctuation
ValueCountFrequency (%)
-1947594
100.0%
Uppercase Letter
ValueCountFrequency (%)
T973797
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common21423534
95.7%
Latin973797
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
04833703
22.6%
14196856
19.6%
22957432
13.8%
-1947594
9.1%
:1947594
9.1%
71229051
 
5.7%
.973797
 
4.5%
3690161
 
3.2%
5674383
 
3.1%
4654166
 
3.1%
Other values (3)1318797
 
6.2%
Latin
ValueCountFrequency (%)
T973797
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22397331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
04833703
21.6%
14196856
18.7%
22957432
13.2%
-1947594
8.7%
:1947594
8.7%
71229051
 
5.5%
T973797
 
4.3%
.973797
 
4.3%
3690161
 
3.1%
5674383
 
3.0%
Other values (4)1972963
8.8%

jerseyNumber
Real number (ℝ≥0)

MISSING

Distinct98
Distinct (%)< 0.1%
Missing42339
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean49.42006296
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:52.968681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median52
Q375
95-th percentile96
Maximum99
Range98
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.91900492
Coefficient of variation (CV)0.6054019993
Kurtosis-1.332975934
Mean49.42006296
Median Absolute Deviation (MAD)27
Skewness0.05194198004
Sum46032713
Variance895.1468555
MonotonicityNot monotonic
2022-11-02T12:01:53.099907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2122218
 
2.3%
2316706
 
1.7%
2416302
 
1.7%
1116233
 
1.7%
9716142
 
1.7%
7215692
 
1.6%
215423
 
1.6%
5514710
 
1.5%
3114278
 
1.5%
7414258
 
1.5%
Other values (88)769496
79.0%
(Missing)42339
 
4.3%
ValueCountFrequency (%)
113212
1.4%
215423
1.6%
35290
 
0.5%
410200
1.0%
56716
0.7%
66470
0.7%
77590
0.8%
89144
0.9%
910500
1.1%
1010350
1.1%
ValueCountFrequency (%)
9913961
1.4%
9813377
1.4%
9716142
1.7%
969190
0.9%
956548
0.7%
9411480
1.2%
939883
1.0%
926914
0.7%
9113981
1.4%
9014076
1.4%

team
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
football
 
42339
WAS
 
39490
KC
 
39490
MIN
 
38863
CAR
 
38863
Other values (24)
774752 

Length

Max length8
Median length3
Mean length3.00782812
Min length2

Characters and Unicode

Total characters2929014
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTB
2nd rowTB
3rd rowTB
4th rowTB
5th rowTB

Common Values

ValueCountFrequency (%)
football42339
 
4.3%
WAS39490
 
4.1%
KC39490
 
4.1%
MIN38863
 
4.0%
CAR38863
 
4.0%
JAX38577
 
4.0%
MIA38577
 
4.0%
DEN38522
 
4.0%
LV38522
 
4.0%
DET36333
 
3.7%
Other values (19)584221
60.0%

Length

2022-11-02T12:01:53.216299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
football42339
 
4.3%
was39490
 
4.1%
kc39490
 
4.1%
min38863
 
4.0%
car38863
 
4.0%
jax38577
 
4.0%
mia38577
 
4.0%
den38522
 
4.0%
lv38522
 
4.0%
det36333
 
3.7%
Other values (19)584221
60.0%

Most occurring characters

ValueCountFrequency (%)
A340780
 
11.6%
I255013
 
8.7%
N241428
 
8.2%
C204116
 
7.0%
E200585
 
6.8%
L196900
 
6.7%
D133364
 
4.6%
T128183
 
4.4%
B123882
 
4.2%
l84678
 
2.9%
Other values (20)1020085
34.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2590302
88.4%
Lowercase Letter338712
 
11.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A340780
13.2%
I255013
 
9.8%
N241428
 
9.3%
C204116
 
7.9%
E200585
 
7.7%
L196900
 
7.6%
D133364
 
5.1%
T128183
 
4.9%
B123882
 
4.8%
H83842
 
3.2%
Other values (14)682209
26.3%
Lowercase Letter
ValueCountFrequency (%)
l84678
25.0%
o84678
25.0%
a42339
12.5%
b42339
12.5%
t42339
12.5%
f42339
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin2929014
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A340780
 
11.6%
I255013
 
8.7%
N241428
 
8.2%
C204116
 
7.0%
E200585
 
6.8%
L196900
 
6.7%
D133364
 
4.6%
T128183
 
4.4%
B123882
 
4.2%
l84678
 
2.9%
Other values (20)1020085
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2929014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A340780
 
11.6%
I255013
 
8.7%
N241428
 
8.2%
C204116
 
7.0%
E200585
 
6.8%
L196900
 
6.7%
D133364
 
4.6%
T128183
 
4.4%
B123882
 
4.2%
l84678
 
2.9%
Other values (20)1020085
34.8%

playDirection
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
left
507449 
right
466348 

Length

Max length5
Median length4
Mean length4.478896526
Min length4

Characters and Unicode

Total characters4361536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowright
2nd rowright
3rd rowright
4th rowright
5th rowright

Common Values

ValueCountFrequency (%)
left507449
52.1%
right466348
47.9%

Length

2022-11-02T12:01:53.309050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-02T12:01:53.399565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
left507449
52.1%
right466348
47.9%

Most occurring characters

ValueCountFrequency (%)
t973797
22.3%
l507449
11.6%
e507449
11.6%
f507449
11.6%
r466348
10.7%
i466348
10.7%
g466348
10.7%
h466348
10.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4361536
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t973797
22.3%
l507449
11.6%
e507449
11.6%
f507449
11.6%
r466348
10.7%
i466348
10.7%
g466348
10.7%
h466348
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin4361536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t973797
22.3%
l507449
11.6%
e507449
11.6%
f507449
11.6%
r466348
10.7%
i466348
10.7%
g466348
10.7%
h466348
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII4361536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t973797
22.3%
l507449
11.6%
e507449
11.6%
f507449
11.6%
r466348
10.7%
i466348
10.7%
g466348
10.7%
h466348
10.7%

x
Real number (ℝ≥0)

Distinct11706
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.44660441
Minimum0.94
Maximum121.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:53.492331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.94
5-th percentile22.04
Q140.17
median58.8
Q378.67
95-th percentile98.82
Maximum121.15
Range120.21
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation23.94899835
Coefficient of variation (CV)0.4028657076
Kurtosis-0.8539693484
Mean59.44660441
Median Absolute Deviation (MAD)19.22
Skewness0.07121546106
Sum57888925.03
Variance573.5545218
MonotonicityNot monotonic
2022-11-02T12:01:53.624661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.76197
 
< 0.1%
56.45195
 
< 0.1%
61.05187
 
< 0.1%
39.33186
 
< 0.1%
35.72184
 
< 0.1%
33.45184
 
< 0.1%
33.58180
 
< 0.1%
40.84180
 
< 0.1%
40.59179
 
< 0.1%
33.98179
 
< 0.1%
Other values (11696)971946
99.8%
ValueCountFrequency (%)
0.941
< 0.1%
12
< 0.1%
1.011
< 0.1%
1.031
< 0.1%
1.041
< 0.1%
1.072
< 0.1%
1.11
< 0.1%
1.121
< 0.1%
1.171
< 0.1%
1.182
< 0.1%
ValueCountFrequency (%)
121.152
< 0.1%
121.142
< 0.1%
121.112
< 0.1%
121.081
< 0.1%
121.031
< 0.1%
120.981
< 0.1%
120.921
< 0.1%
120.851
< 0.1%
120.771
< 0.1%
120.681
< 0.1%

y
Real number (ℝ)

Distinct5399
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.70109764
Minimum-2.55
Maximum55.94
Zeros1
Zeros (%)< 0.1%
Negative42
Negative (%)< 0.1%
Memory size7.4 MiB
2022-11-02T12:01:53.757215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.55
5-th percentile11.43
Q121.83
median26.7
Q331.55
95-th percentile42.02
Maximum55.94
Range58.49
Interquartile range (IQR)9.72

Descriptive statistics

Standard deviation8.363680499
Coefficient of variation (CV)0.3132335836
Kurtosis0.2775235246
Mean26.70109764
Median Absolute Deviation (MAD)4.86
Skewness0.004224886474
Sum26001448.78
Variance69.95115149
MonotonicityNot monotonic
2022-11-02T12:01:53.877761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.8981
 
0.1%
23.83979
 
0.1%
23.86972
 
0.1%
23.89967
 
0.1%
23.81965
 
0.1%
23.82930
 
0.1%
23.9928
 
0.1%
23.79915
 
0.1%
23.73909
 
0.1%
23.78909
 
0.1%
Other values (5389)964342
99.0%
ValueCountFrequency (%)
-2.551
< 0.1%
-2.541
< 0.1%
-2.531
< 0.1%
-2.51
< 0.1%
-2.491
< 0.1%
-2.431
< 0.1%
-2.411
< 0.1%
-2.321
< 0.1%
-2.31
< 0.1%
-2.181
< 0.1%
ValueCountFrequency (%)
55.941
< 0.1%
55.311
< 0.1%
55.031
< 0.1%
54.942
< 0.1%
54.891
< 0.1%
54.881
< 0.1%
54.821
< 0.1%
54.81
< 0.1%
54.781
< 0.1%
54.682
< 0.1%

s
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2139
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.616375723
Minimum0
Maximum28.78
Zeros62663
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:54.009495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.78
median2.17
Q33.86
95-th percentile6.81
Maximum28.78
Range28.78
Interquartile range (IQR)3.08

Descriptive statistics

Standard deviation2.403339724
Coefficient of variation (CV)0.9185759152
Kurtosis14.23207695
Mean2.616375723
Median Absolute Deviation (MAD)1.51
Skewness2.336976597
Sum2547818.83
Variance5.77604183
MonotonicityNot monotonic
2022-11-02T12:01:54.133642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062663
 
6.4%
0.0115252
 
1.6%
0.028659
 
0.9%
0.036204
 
0.6%
0.045130
 
0.5%
0.054374
 
0.4%
0.063838
 
0.4%
0.073526
 
0.4%
0.083261
 
0.3%
0.093208
 
0.3%
Other values (2129)857682
88.1%
ValueCountFrequency (%)
062663
6.4%
0.0115252
 
1.6%
0.028659
 
0.9%
0.036204
 
0.6%
0.045130
 
0.5%
0.054374
 
0.4%
0.063838
 
0.4%
0.073526
 
0.4%
0.083261
 
0.3%
0.093208
 
0.3%
ValueCountFrequency (%)
28.781
< 0.1%
28.611
< 0.1%
28.381
< 0.1%
28.041
< 0.1%
27.871
< 0.1%
27.741
< 0.1%
27.431
< 0.1%
27.241
< 0.1%
27.131
< 0.1%
27.041
< 0.1%

a
Real number (ℝ≥0)

ZEROS

Distinct1549
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.80187497
Minimum0
Maximum28.4
Zeros58410
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:54.264769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.73
median1.55
Q32.59
95-th percentile4.48
Maximum28.4
Range28.4
Interquartile range (IQR)1.86

Descriptive statistics

Standard deviation1.446293637
Coefficient of variation (CV)0.8026603738
Kurtosis7.029842627
Mean1.80187497
Median Absolute Deviation (MAD)0.91
Skewness1.491214838
Sum1754660.44
Variance2.091765284
MonotonicityNot monotonic
2022-11-02T12:01:54.384706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058410
 
6.0%
0.0111797
 
1.2%
0.026611
 
0.7%
0.035156
 
0.5%
0.044135
 
0.4%
0.053517
 
0.4%
1.123142
 
0.3%
1.193141
 
0.3%
1.373127
 
0.3%
1.143126
 
0.3%
Other values (1539)871635
89.5%
ValueCountFrequency (%)
058410
6.0%
0.0111797
 
1.2%
0.026611
 
0.7%
0.035156
 
0.5%
0.044135
 
0.4%
0.053517
 
0.4%
0.062958
 
0.3%
0.072744
 
0.3%
0.082387
 
0.2%
0.092204
 
0.2%
ValueCountFrequency (%)
28.41
< 0.1%
27.221
< 0.1%
26.731
< 0.1%
26.031
< 0.1%
26.021
< 0.1%
25.861
< 0.1%
25.421
< 0.1%
25.011
< 0.1%
24.811
< 0.1%
24.591
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct537
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2649353921
Minimum0
Maximum8.9
Zeros61876
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:54.516816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.08
median0.22
Q30.39
95-th percentile0.68
Maximum8.9
Range8.9
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.2578158758
Coefficient of variation (CV)0.9731273492
Kurtosis51.68517049
Mean0.2649353921
Median Absolute Deviation (MAD)0.15
Skewness4.260855707
Sum257993.29
Variance0.06646902581
MonotonicityNot monotonic
2022-11-02T12:01:54.640316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
061876
 
6.4%
0.0150588
 
5.2%
0.0229000
 
3.0%
0.0322494
 
2.3%
0.0419722
 
2.0%
0.0518535
 
1.9%
0.217952
 
1.8%
0.1717891
 
1.8%
0.1817857
 
1.8%
0.1617851
 
1.8%
Other values (527)700031
71.9%
ValueCountFrequency (%)
061876
6.4%
0.0150588
5.2%
0.0229000
3.0%
0.0322494
 
2.3%
0.0419722
 
2.0%
0.0518535
 
1.9%
0.0617634
 
1.8%
0.0717233
 
1.8%
0.0816904
 
1.7%
0.0916761
 
1.7%
ValueCountFrequency (%)
8.91
< 0.1%
7.591
< 0.1%
7.11
< 0.1%
6.831
< 0.1%
6.561
< 0.1%
6.521
< 0.1%
6.471
< 0.1%
6.281
< 0.1%
6.271
< 0.1%
6.251
< 0.1%

o
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.9%
Missing42339
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.2660642
Minimum0
Maximum360
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:54.769528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.85
Q189.81
median178.39
Q3270.31
95-th percentile329.92
Maximum360
Range360
Interquartile range (IQR)180.5

Descriptive statistics

Standard deviation99.30368715
Coefficient of variation (CV)0.5508728867
Kurtosis-1.375388446
Mean180.2660642
Median Absolute Deviation (MAD)90.26
Skewness0.006370240159
Sum167910267.6
Variance9861.222281
MonotonicityNot monotonic
2022-11-02T12:01:54.890593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
901992
 
0.2%
266.51101
 
< 0.1%
84.4897
 
< 0.1%
266.997
 
< 0.1%
273.3795
 
< 0.1%
274.5494
 
< 0.1%
92.6392
 
< 0.1%
80.0492
 
< 0.1%
85.591
 
< 0.1%
270.3491
 
< 0.1%
Other values (35991)928616
95.4%
(Missing)42339
 
4.3%
ValueCountFrequency (%)
08
 
< 0.1%
0.0112
< 0.1%
0.0210
 
< 0.1%
0.0314
< 0.1%
0.0416
< 0.1%
0.0510
 
< 0.1%
0.067
 
< 0.1%
0.0729
< 0.1%
0.0821
< 0.1%
0.0911
 
< 0.1%
ValueCountFrequency (%)
36010
< 0.1%
359.9921
< 0.1%
359.9820
< 0.1%
359.978
 
< 0.1%
359.9618
< 0.1%
359.9519
< 0.1%
359.9416
< 0.1%
359.9313
< 0.1%
359.9212
< 0.1%
359.9119
< 0.1%

dir
Real number (ℝ≥0)

MISSING

Distinct36001
Distinct (%)3.9%
Missing42339
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean180.3159753
Minimum0
Maximum360
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.4 MiB
2022-11-02T12:01:55.021889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.01
Q190.56
median179.98
Q3270.48
95-th percentile336.32
Maximum360
Range360
Interquartile range (IQR)179.92

Descriptive statistics

Standard deviation101.0175078
Coefficient of variation (CV)0.5602249477
Kurtosis-1.289176848
Mean180.3159753
Median Absolute Deviation (MAD)89.96
Skewness2.518902752 × 10-5
Sum167956757.7
Variance10204.53689
MonotonicityNot monotonic
2022-11-02T12:01:55.145066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.2465
 
< 0.1%
91.8865
 
< 0.1%
88.2465
 
< 0.1%
93.0465
 
< 0.1%
96.1765
 
< 0.1%
91.8164
 
< 0.1%
268.4764
 
< 0.1%
94.3964
 
< 0.1%
268.3764
 
< 0.1%
269.4864
 
< 0.1%
Other values (35991)930813
95.6%
(Missing)42339
 
4.3%
ValueCountFrequency (%)
024
< 0.1%
0.0116
< 0.1%
0.0219
< 0.1%
0.0328
< 0.1%
0.0412
< 0.1%
0.0516
< 0.1%
0.0620
< 0.1%
0.0711
 
< 0.1%
0.0820
< 0.1%
0.0917
< 0.1%
ValueCountFrequency (%)
3608
 
< 0.1%
359.9921
< 0.1%
359.9826
< 0.1%
359.9724
< 0.1%
359.9611
< 0.1%
359.9524
< 0.1%
359.9414
< 0.1%
359.9318
< 0.1%
359.9217
< 0.1%
359.9120
< 0.1%

event
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.4 MiB
None
897644 
ball_snap
 
23046
pass_forward
 
20585
autoevent_passforward
 
10488
autoevent_ballsnap
 
10327
Other values (15)
 
11707

Length

Max length25
Median length4
Mean length4.699426061
Min length3

Characters and Unicode

Total characters4576287
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowNone
5th rowNone

Common Values

ValueCountFrequency (%)
None897644
92.2%
ball_snap23046
 
2.4%
pass_forward20585
 
2.1%
autoevent_passforward10488
 
1.1%
autoevent_ballsnap10327
 
1.1%
play_action5750
 
0.6%
run1173
 
0.1%
qb_sack1104
 
0.1%
pass_arrived897
 
0.1%
autoevent_passinterrupted667
 
0.1%
Other values (10)2116
 
0.2%

Length

2022-11-02T12:01:55.268465image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
none897644
92.2%
ball_snap23046
 
2.4%
pass_forward20585
 
2.1%
autoevent_passforward10488
 
1.1%
autoevent_ballsnap10327
 
1.1%
play_action5750
 
0.6%
run1173
 
0.1%
qb_sack1104
 
0.1%
pass_arrived897
 
0.1%
autoevent_passinterrupted667
 
0.1%
Other values (10)2116
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n961906
21.0%
o957559
20.9%
e944518
20.6%
N897644
19.6%
a166727
 
3.6%
s102097
 
2.2%
_75164
 
1.6%
p73830
 
1.6%
l73048
 
1.6%
r66815
 
1.5%
Other values (15)256979
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3603479
78.7%
Uppercase Letter897644
 
19.6%
Connector Punctuation75164
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n961906
26.7%
o957559
26.6%
e944518
26.2%
a166727
 
4.6%
s102097
 
2.8%
p73830
 
2.0%
l73048
 
2.0%
r66815
 
1.9%
t52739
 
1.5%
b34661
 
1.0%
Other values (13)169579
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N897644
100.0%
Connector Punctuation
ValueCountFrequency (%)
_75164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4501123
98.4%
Common75164
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n961906
21.4%
o957559
21.3%
e944518
21.0%
N897644
19.9%
a166727
 
3.7%
s102097
 
2.3%
p73830
 
1.6%
l73048
 
1.6%
r66815
 
1.5%
t52739
 
1.2%
Other values (14)204240
 
4.5%
Common
ValueCountFrequency (%)
_75164
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4576287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n961906
21.0%
o957559
20.9%
e944518
20.6%
N897644
19.6%
a166727
 
3.6%
s102097
 
2.2%
_75164
 
1.6%
p73830
 
1.6%
l73048
 
1.6%
r66815
 
1.5%
Other values (15)256979
 
5.6%

Interactions

2022-11-02T12:01:44.116431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:09.134506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:12.368152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:15.418522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:18.808173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:21.896518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:24.981274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:28.236494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:31.326341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:34.426882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:37.702134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:40.771458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:44.392436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:09.402419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:12.630655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:15.693207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:19.072812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:22.161467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:25.249130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:28.497599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:31.591257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:34.694961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:37.962162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:41.050423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:44.654294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:01:29.534596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:01:35.896714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-02T12:01:40.508146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-02T12:01:43.848126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-02T12:01:55.529893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T12:01:55.681200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T12:01:55.825960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T12:01:55.972522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T12:01:56.107111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-02T12:01:56.216433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T12:01:47.600044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T12:01:48.727594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-02T12:01:50.460614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-02T12:01:51.075777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
020211014007625511.012021-10-15T00:23:39.20012.0TBright33.9923.880.00.00.0091.7367.89None
120211014007625511.022021-10-15T00:23:39.30012.0TBright33.9923.880.00.00.0091.7357.67None
220211014007625511.032021-10-15T00:23:39.40012.0TBright34.0023.890.00.00.0191.7349.03None
320211014007625511.042021-10-15T00:23:39.50012.0TBright34.0023.890.00.00.0091.7347.57None
420211014007625511.052021-10-15T00:23:39.60012.0TBright34.0023.890.00.00.0091.7350.68None
520211014007625511.062021-10-15T00:23:39.70012.0TBright34.0023.890.00.00.0091.0246.28ball_snap
620211014007625511.072021-10-15T00:23:39.80012.0TBright33.9923.890.00.00.0091.0234.90autoevent_ballsnap
720211014007625511.082021-10-15T00:23:39.90012.0TBright33.9923.890.00.00.0191.022.17None
820211014007625511.092021-10-15T00:23:40.00012.0TBright33.9823.890.00.00.0191.02320.86None
920211014007625511.0102021-10-15T00:23:40.10012.0TBright33.9723.890.00.00.0191.02297.69None

Last rows

gameIdplayIdnflIdframeIdtimejerseyNumberteamplayDirectionxysadisodirevent
97378720211018003998NaN332021-10-19T03:18:54.200NaNfootballright94.2123.250.690.990.16NaNNaNNone
97378820211018003998NaN342021-10-19T03:18:54.300NaNfootballright94.4523.320.730.660.25NaNNaNNone
97378920211018003998NaN352021-10-19T03:18:54.400NaNfootballright94.5923.330.770.700.13NaNNaNNone
97379020211018003998NaN362021-10-19T03:18:54.500NaNfootballright94.6723.320.770.710.09NaNNaNNone
97379120211018003998NaN372021-10-19T03:18:54.600NaNfootballright94.7523.320.770.690.08NaNNaNrun
97379220211018003998NaN382021-10-19T03:18:54.700NaNfootballright94.8723.350.820.780.12NaNNaNNone
97379320211018003998NaN392021-10-19T03:18:54.800NaNfootballright95.0323.421.051.090.18NaNNaNNone
97379420211018003998NaN402021-10-19T03:18:54.900NaNfootballright95.4223.651.912.380.45NaNNaNNone
97379520211018003998NaN412021-10-19T03:18:55.000NaNfootballright95.7923.862.653.680.43NaNNaNNone
97379620211018003998NaN422021-10-19T03:18:55.100NaNfootballright96.2424.133.630.440.52NaNNaNNone